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Conference Papers Year : 2022

Conceptual Similarity for Subjective Tags

Abstract

Tagging in the context of online resources is a fundamental addition to search systems. Tags assist with the indexing, management, and retrieval of online products and services to answer complex user queries. Traditional methods of matching user queries with tags either rely on cosine similarity, or employ semantic similarity models that fail to recognize conceptual connections between tags, e.g. ambiance and music. In this work, we focus on subjective tags which characterize subjective aspects of a product or service. We propose conceptual similarity to leverage conceptual awareness when assessing similarity between tags. We also provide a simple cost-effective pipeline to automatically generate data in order to train the conceptual similarity model. We show that our pipeline generates high-quality datasets, and evaluate the similarity model both systematically and on a downstream application. Experiments show that conceptual similarity outperforms existing work when using subjective tags.
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Dates and versions

hal-03866253 , version 1 (22-11-2022)

Identifiers

  • HAL Id : hal-03866253 , version 1

Cite

Yacine Gaci, Boualem Benatallah, Fabio Casati, Khalid Benabdeslem. Conceptual Similarity for Subjective Tags. AACL-IJCNLP 2022 (2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing), Nov 2022, Online, Taiwan. ⟨hal-03866253⟩
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